Project Summary/Abstract The autoimmune disease vitiligo, which spontaneously occurs in patients undergoing treatment for melanoma has been documented since the 60?s, but only recently has this disease been appreciated as a good prognosis marker. This recent finding in human patients can be confirmed by our published work in mice which demonstrates that the establishment of vitiligo is required for protection against tumor rechallenge in the skin and lung. Further characterization of vitiligo-affected skin showed that there is a high prevalence of tumor-specific resident memory (TRM) cells which are indispensable for tumor immunity in the vitiligo-affected skin. In addition, infiltration of TRM in tumors has been observed in patients with improved overall survival but failed to differentiate TRM from other sub-populations of tumor infiltrating lymphocytes (TIL?s) that are found in an antitumor response. Single cell RNA-seq has not been used to elucidate the clonal differentiation of self-reactive T cells in time and space in patients that developed long lasting immunity against cancer. Furthermore, the molecular biology of TRM and function is gravely understudied therefore, this proposal aims to comprehend the unique transcriptional changes that facilitate TRM formation during an immune response against cancer. I hypothesize that a unique transcriptional gene network dictates the formation of resident memory cells which are the primary guardians of the lung against metastatic melanoma. Therefore, Specific Aim 1 will test the dependency on TRM for protection against metastatic melanoma in the lung. While Specific Aim 2 will define a TRM gene expression signature from scRNA-seq of tumor-specific T cells and assess the predictive power as a prognostic biomarker for patients with metastatic melanoma. In accordance with the goals of this project, I aspire to implement and develop the burgeoning field of computational immunology in elucidating the role of TRM in immunity against cancer. I expect to show novel application of bioinformatics in generating insightful interpretation of gene expression data from patient tumor biopsies. This has a direct clinical significance in showing the importance of tailoring immunotherapies to a TRM formation response.